Papers

Purpose: To develop and evaluate DosimeTron, an agentic AI system for automated patient-specific MC internal radiation dosimetry in PET/CT examinations. Materials and Methods: In this retrospective study, DosimeTron was evaluated on a publicly available PSMA-PET/CT dataset comprising 597 studies from 378 male patients acquired on three scanner models (18-F, n = 369; 68-Ga, n = 228). The system uses GPT-5.2 as its reasoning engine and 23 tools exposed via four Model Context Protocol servers, automating DICOM metadata extraction, image preprocessing, MC simulation, organ segmentation, and dosimetric reporting through natural-language interaction. Agentic performance was assessed using diverse prompt templates spanning single-turn instructions of varying specificity and multi-turn conversational exchanges, monitored via OpenTelemetry traces. Dosimetric accuracy was validated against OpenDose3D across 114 cases and 22 organs using Pearson's r, Lin's concordance correlation coefficient (CCC), and Bland-Altman analysis. Results: Across all prompt templates and all runs, no execution failures, pipeline errors, or hallucinated outputs were observed. Pearson's r ranged from 0.965 to 1.000 (median 0.997; all p < 0.001) and CCC from 0.963 to 1.000 (median 0.996). Mean absolute percentage difference was below 5% for 19 of 22 organs (median 2.5%). Total per-study processing time (SD) was 32.3 (6.0) minutes. Conclusion: DosimeTron autonomously executed complex dosimetry pipelines across diverse prompt configurations and achieved high dosimetric agreement with OpenDose3D at clinically acceptable processing times, demonstrating the feasibility of agentic AI for patient-specific Monte Carlo dosimetry in PET/CT.

Accurate parameter selection is fundamental to gyrokinetic plasma simulations, yet current practices rely heavily on manual literature reviews, leading to inefficiencies and inconsistencies. We introduce Plasma GraphRAG, a novel framework that integrates Graph Retrieval-Augmented Generation (GraphRAG) with large language models (LLMs) for automated, physics-grounded parameter range identification. By constructing a domain-specific knowledge graph from curated plasma literature and enabling structured retrieval over graph-anchored entities and relations, Plasma GraphRAG enables LLMs to generate accurate, context-aware recommendations. Extensive evaluations across five metrics, comprehensiveness, diversity, grounding, hallucination, and empowerment, demonstrate that Plasma GraphRAG outperforms vanilla RAG by over $10\%$ in overall quality and reduces hallucination rates by up to $25\%$. {Beyond enhancing simulation reliability, Plasma GraphRAG offers a methodology for accelerating scientific discovery across complex, data-rich domains.

The nonlinear dynamics of transverse and polarization modes of a broad-area vertical-cavity surface-emitting laser (BA-VCSEL) exhibit, without any external perturbation, chaos with high correlation dimension, large bandwidth (BW), and good spectral flatness over a wide range of currents. We leverage this for high bit-rate entropy generation and random number generation (RNG), passing the NIST tests with rates up to 150~Gb/s, and observe a correlation between the correlation dimension and the number of passed NIST tests. The RNG shows consistent performance across a wide range of parameters. In contrast to other setups, our system does not require optical feedback or optical injection to generate chaos, making it simple, compact and robust.

The interaction between cavitation bubbles and surrounding droplets plays a central role in applications such as surface cleaning, ultrasonic emulsification, and therapeutic delivery. These processes depend on bubble-driven microjets that drive the deformation and breakup of the droplets, which are significantly influenced by geometric confinements. Here, we investigate the hydrodynamic interaction between cavitation bubbles and oil droplets within a thin water layer considering the coupling confinements of a free surface and a rigid wall. We reveal two distinct regimes of droplet response to cavitation bubble collapse: the rupture regime, where oil droplets fragment into smaller droplets, and the no-rupture regime, where the droplet remains intact. By deriving a non-dimensional Kelvin impulse to represent the momentum of the bubble-induced jet, we establish a scaling law that correlates the criterion for droplet rupture to a characteristic Weber number and the bubble-to-droplet size ratio for the first time. This framework delineates the rupture boundary and even extends to predict the rupture of particle-laden droplets driven by cavitation bubbles. Our findings reveal the hydrodynamic principles underlying the cavitation bubble-driven droplet rupture and provide predictive criteria for controlling performance in engineering and biomedical systems involving cavitation bubble dynamics.

Nonlinear Four-wave mixing processes are a powerful technique to unravel ultrafast dynamics in solid-state systems. Here, we employ attosecond four-wave mixing spectroscopy with one extreme ultraviolet (XUV) pump and two independently delayed, noncollinear near-infrared (NIR) probes to resolve the ultrafast decoherence of both dipole-allowed and dipole-forbidden core excitons at the Na+ L2,3 edge in sodium fluoride (NaF). The decoherence times of the core excitons are observed to be much faster than the 8 fs limit of the instrument response time, which is attributed to strong exciton-phonon coupling. Furthermore, polarization control of the NIR probes (Perpendicular and parallel polarizations) reveals that the bright core excitons exhibit s-like orbital angular momentum, while dark core excitons, reached by two-photon excitation, exhibit p-like angular momentum.

Metalenses have emerged as a powerful platform for compact wavefront engineering; however, their performance stability under refractive index fluctuations induced by environmental perturbations, such as temperature shifts, remains a critical concern. Here, we demonstrate the intrinsic refractive index robustness of dielectric metalenses and elucidate its physical origin. By parametrically sweeping the refractive index, we observe that the metalens maintains a stable focal profile with negligible deviations in best-focus position and spot size over a broad range of variations. We identify that this robustness arises from the structural invariance of the zone boundaries: despite index-induced local phase deformations, the spatial locations of the 2{\pi} phase-reset boundaries remain stationary, thereby maintaining the effective wavefront gradient. Furthermore, we reveal that this robustness enables a "quasi-scale-invariant" focusing behavior, where the focusing performance follows a predictable linear trend under uniform geometric scaling even in the presence of material dispersion. Our findings suggest that metalenses can maintain stable focusing behavior against refractive index variations that may arise from unavoidable environmental perturbations in practical optical systems.

Representation of cities as organisms with metabolic processes is a useful analogy for urban design, development and sustainability. Urban metabolism can be modeled by representing urban systems as networks. The various networks included in a city's metabolism are interdependent in complex ways. Thus, understanding the interaction among these networks is essential to understanding how a healthy urban metabolism is sustained and how injuries to the metabolic system can "heal". It is particularly important to understand how disruptions to one system in an urban area affect the functioning of other systems. Using distribution-level data from a real U.S. city on the electricity distribution system and road geometry, we apply connected network modeling to two critical inter-connected urban infrastructure sectors: energy and transportation. We quantify the robustness of these interdependent networks by evaluating the connectivity disruptions that may occur due to natural or synthetic disruptive events, using both unweighted and weighted metrics.

A first-of-its-kind experiment was conducted in the DIII-D tokamak under WEST similarity constraints on plasma shape and core parameters. This work presents a detailed transport study comparing a reference regime dominated by intrinsic carbon radiation and a high-radiation regime resulting from controlled tungsten (W) injection using the Laser Blow-Off system, with a core tungsten concentration $n_{\mathrm{W}}/n_e \sim 3\times 10^{-4}$ and a radiated-power fraction $f_\mathrm{rad}>0.5$. The W-induced radiative cooling lowered the electron temperature, thereby decreasing $T_e/T_i$ and stabilizing trapped-electron-mode (TEM) turbulence. This transition in turbulence regime reduced momentum and ion thermal diffusivities, yielding ion temperature peaking and a factor-of-two increase in toroidal rotation. At the outer plasma region, enhanced $E\timesB$ shear and increased collisionality further suppressed ion-scale turbulence, causing a sharp drop in ion heat flux. Consequently, impurity transport, predominantly turbulent in the low-radiation regime, acquired a strong neoclassical inward W convection during radiative cooling, bootstrapping the cooling cycle. Despite $f_\mathrm{rad}>0.5$, radiative collapse was not observed, likely owing to collisional ion-to-electron energy exchange acting as an electron-energy reservoir, together with $1/1$ MHD activity modulating the radiated power through core impurity neoclassical $T_i$-screening. These results support preparation for a tungsten wall change in DIII-D by elucidating tungsten-induced turbulence stabilization. They also provide key insights for interpreting plasma performance in WEST and are relevant to future reactors expected to operate with radiating tungsten-walled plasmas.

Gyrokinetic simulations are essential for understanding and controlling turbulence in fusion plasmas, yet they are oftentimes implemented in legacy codebases, in many cases CPU-bound. These are both hard to maintain and especially incompatible with optimization and ML workflows. gyaradax is a minimal JAX/CUDA solver for local flux-tube gyrokinetics. We base our implementation on GKW (Peeters et al., 2009), but with added native GPU acceleration and automatic differentiation. We validate gyaradax against analytical cases and empirical benchmarks, achieving formal agreement and statistical parity with GKW alongside a substantial speedup. We deliberately and extensively utilized agentic workflows in this project. A key contribution is showing that coding agents, guided by human expertise, structured prompting, and measurable progress through unit testing enabled extremely fast translation of complex Fortran code, and further optimizations. Gyaradax facilitates research at the intersection of ML and plasma physics. We showcase this through practical examples in inverse problems and sensitivity analysis.

The discovery of topological phases has introduced a new dimension to materials science. Three-dimensional (3D) topological insulators (TIs) are a remarkable class of matter that is insulating in the bulk while hosting conductive topological surface states (TSSs) with unique charge and spin properties. High-order harmonic generation (HHG) has emerged as a powerful tool to probe condensed matter systems by providing insights into their electronic structure and dynamic behavior. Here, we investigate HHG in the prototype 3D-TI Bi$_2$Se$_3$. We demonstrate that the contributions of bulk and surface states to the harmonic emission can be controlled by tuning the thickness of thin film samples. An ultrathin (6 nm) film substantially enhances HHG from the surface states, while the bulk states dominate HHG in a thicker (50 nm) film. By applying a quasi-static terahertz perturbing field, we disentangle the bulk and surface responses and reveal the significant impact of the surface states' shift vector and Berry curvature on HHG. Our study provides effective methods for isolating the optical responses of TSSs from those of the bulk, which opens the door to resolving an ongoing debate regarding whether it is possible to reliably extract topological signatures in HHG.

Understanding ligand properties is essential for computational high-throughput screening of transition metal complexes. However, ligand properties such as net charge and other information such as their application area are often absent or inconsistently recorded in crystallographic datasets. Here, we construct a ligand dataset from 126,985 mononuclear transition metal complexes curated from the Cambridge Structural Database. Using an iterative charge-balancing workflow that combines complex charges, metal oxidation states, and consensus across crystallographic observations, we confidently assign net charges to 66,810 ligands among 94,581 identified unique ligand structures to curate the Boston Open-Shell Ligand (BOS-Lig) dataset. The workflow assigns ligand charges in homoleptic complexes first and then iteratively propagates these assignments across heteroleptic environments, allowing charges to be inferred even when direct charge information is unavailable. We analyze cases where simple heuristics such as the octet rule would have failed and introduce a purity metric to identify when our charge assignments may be incorrect. Each ligand is also classified in terms of its metal coordinating atoms and whether there are multiple variants (i.e., hemilability). We then link complexes to their associated journal abstracts and apply a topic-modeling workflow to link 25,146 ligands with functional application areas spanning reactivity, redox chemistry, biological chemistry, and photophysical chemistry. Together, we provide an experimentally grounded dataset of ligand chemical space that connects charge and functional application as a foundation for computational screening and data-driven ligand design.

To optimize diffusion MRI acquisition protocols for IMPULSED model at clinical 3T scanner using Bayesian experimental design, enabling accurate cellular-scale parameter estimation under realistic scan time and scanner hardware constraints. Expected Information Gain (EIG) was used as the optimization objective to maximize the information content of acquired measurements for IMPULSED model fitting. Bayesian optimization with Gaussian process surrogates efficiently searched the high-dimensional acquisition parameter space, including pulse types (PGSE, OGSEn1, and OGSEn2), diffusion times, and b-values. Optimized protocols were systematically evaluated against a heuristically designed baseline protocol through simulation studies assessing classification accuracy and parameter estimation performance across SNR levels of 5-40. Robustness to optimization assumptions was examined by varying prior distributions and assumed SNR. In-vivo validation was performed using canine tumor data acquired at 3T. The optimized protocol eliminated OGSEn2 acquisitions, concentrated measurements at high b-values, employing concurrently optimized diffusion timing. Compared to the baseline protocol, the optimized design achieved superior classification accuracy for distinguishing cell populations and reduced parameter estimation error across biologically relevant parameter ranges at various SNRs. Performance advantages were consistent across diverse optimization scenarios, demonstrating robustness to prior knowledge and noise assumptions. In-vivo parameter maps showed substantially improved quality and smoothness. Bayesian optimization substantially improves IMPULSED acquisition design for clinical 3T scanners. This principled, algorithm-agnostic framework enables accurate diffusion MRI cytometry under clinical constraints, with potential applications to tumor characterization and treatment monitoring.

Efficiently solving the Fokker-Planck equation (FPE) is central to analyzing complex parameterized stochastic systems. However, current numerical methods lack parallel computation capabilities across varying conditions, severely limiting comprehensive parameter exploration and transient analysis. This paper introduces a deep learning-based pseudo-analytical probability solution (PAPS) that, via a single training process, simultaneously resolves transient FPE solutions for arbitrary multi-modal initial distributions, system parameters, and time points. The core idea is to unify initial, transient, and stationary distributions via Gaussian mixture distributions (GMDs) and develop a constraint-preserving autoencoder that bijectively maps constrained GMD parameters to unconstrained, low-dimensional latent representations. In this representation space, the panoramic transient dynamics across varying initial conditions and system parameters can be modeled by a single evolution network. Extensive experiments on paradigmatic systems demonstrate that the proposed PAPS maintains high accuracy while achieving inference speeds four orders of magnitude faster than GPU-accelerated Monte Carlo simulations. This efficiency leap enables previously intractable real-time parameter sweeps and systematic investigations of stochastic bifurcations. By decoupling representation learning from physics-informed transient dynamics, our work establishes a scalable paradigm for probabilistic modeling of multi-dimensional, parameterized stochastic systems.

Current theories of high-temperature superconductivity in flat-band systems predict a linear dependence of the transition temperature on the attractive interaction, $T_c(U) = c|U|$. However, the value of $c$ and the full nonlinear $T_c(U)$ curve—with a maximum at large $|U|$—are known beyond mean-field and quantum geometry estimates only for systems with isolated flat bands. Using a controlled diagrammatic Monte Carlo technique, we trace the onset of superfluid response in the Lieb lattice with attractive Hubbard interaction. Focusing on the half-filled flat-band case, where the ordering mechanism differs fundamentally from both BCS and BEC (preformed Cooper pair) scenarios, we find that the pairing response diverges linearly with decreasing temperature over a broad range of $U$, leading to a sharp crossover to long-range correlations at a characteristic temperature $T_*$, which provides a controlled upper bound on $T_c$. The highest $T_*$ occurs when all three bands touch at a single momentum point, potentially corresponding to high $T_c$ values.

Non-Maxwellian electron velocity distributions (eVDFs) are ubiquitous in collisionless plasmas. For example, various types of non-Maxwellian eVDFs exist in magnetic reconnection jets in the Earth's magnetotail. At thermal energies, eVDF can be flat-topped due to electron trapping associated with magnetic reconnection. However, the occurrence of such eVDFs in magnetotail reconnection remains largely unconstrained. Here, we statistically investigate flat-top eVDFs in fast plasma jets in the magnetotail using a new method for classifying eVDFs. We show that only $\sim 7\%$ of the eVDFs in the jets are flat-tops. Nevertheless, we find that most jets exhibit flat-top eVDFs, indicating that this signature of parallel acceleration and electron streaming is characteristic of the jets. We find that these flat-top eVDFs are localized within an ion-inertial-length-scale region near the edges of the current sheet and close to the reconnection region. Our results highlight the importance of flat-top eVDFs in non-local thermodynamic equilibrium collisionless plasmas.

Direct Air Carbon Capture and Storage (DACCS) can mitigate hard-to-abate emissions, e.g. from transport or industry. However, there is a wide variety of cost estimates for DACCS, driven, to a significant extent, by differences in electricity cost. At the same time, there is a notable gap in research that integrates direct air capturing systems into long-term energy system models. We separate direct air capturing, carbon transport, and carbon storage and integrate them into a European capacity expansion model for a fully decarbonised electricity system in 2050. We explore how two dimensions affect the total system costs of DACCS. The first dimension is the availability of CO2 storage locations: In one analysis, storage locations are restricted to offshore storage locations in the North Sea only, i.e. depleted natural gas fields. The alternative analysis comprises suitable storage locations distributed across Europe, including onshore. We find that limiting CO2 storage to North Sea sites increases overall capture costs by approximately 10 %. The second dimension is whether DACCS is analysed as stand-alone or integrated into the electricity system. We differentiate between three alternatives: fully isolated, fully integrated, and retrospectively added to an existing system. We find that neglecting system integration - i.e. treating direct air capture system as a stand-alone technology - increases capture costs by up to 30 %.

Ptychographic reconstructions in reflection geometries are commonly interpreted with the same two-dimensional thin-sample model used in transmission, yet the validity of this approximation has not been established. We develop a three-dimensional weak-scattering description of reflection ptychography and derive explicit thickness criteria for when a two-dimensional model remains accurate. Because the sampled axial spatial frequency range is dominated by the rotation of the Ewald sphere rather than its curvature, reflection geometries impose far stricter thin-sample conditions than transmission geometries. The allowable thickness is reduced by one to two orders of magnitude for a representative extreme ultraviolet geometry, depending on the tolerance for appearance of artifacts. Simulations verify that conventional two-dimensional reconstructions may exhibit the thickness-dependent artifacts as predicted by the theory, with particularly strong distortions near specular Bragg minima. We further show that incorporating the correct depth-dependent propagation into the forward model resolves these distortions and enables recovery of sample thickness. These results establish practical validity limits for two-dimensional reflection ptychography and identify a path toward quantitative depth-sensitive reconstructions at all geometries.

Refractory compositionally complex alloys (RCCAs) are considered the next generation high-temperature materials. However, their high-dimensional composition spaces are too large to explore by traditional density functional theory or experimental means, making new RCCA discovery slow and cumbersome. This work has addressed these challenges with an integrated composition design framework that can efficiently and exhaustively explore the relationship between the compositions and two fundamental aspects: 1) the phase stability, including the target body-centered cubic (BCC) phase and its competing phases (hexagonal closed-pack (HCP) structures, Laves and B2 intermetallic phases), and 2) the mechanical properties. This framework is demonstrated with RCCAs within nine refractory metals (Ti, V, Cr, Zr, Nb, Mo, Hf, Ta, and W). Theory-guided machine learning (ML) models were employed to find the composition-mechanical property relationship of RCCAs, where the established theory is used to supplement the yield strength data at ultra-high temperature, and a forward sequential feature selection (SFS) is used to determine feature selection. The resulting ML model for temperature-dependent yield strength was found to have an R_squared value of 0.98 over the entire temperature range (from 0 to 2000 K). The impact of each constituent element on the six key properties is evaluated. The addition of Nb tends to stabilize the BCC phase and the addition of Ti improves the ductility of RCCAs. Combined with all methods involved in this framework, the on-demand designer allows the alloy designers to have all properties for any RCCA compositions and narrow down the composition space by applying custom screening criteria. The output from the predictor and screener provides valuable guidance for our experimental study of RCCAs and accelerates the pace of materials discovery.

Speckle metrology exploits the high sensitivity of scattered fields to parameters of interest, yet this also leaves measurements vulnerable to unintended perturbations. Here we employ transmission matrix formalism to engineer light fields that produce speckle correlation "revivals", selectively reducing response to a chosen parameter. In a multimode fiber scattering system, we suppress bending-induced correlation changes over a limited curvature range without strongly degrading wavelength sensitivity, opening a route to tailored, parameter-specific sensitivities of speckle-based measurements.

The goal of ensemble forecasting is to maximise sharpness subject to reliability. Marginal reliability means that, over all cases, the ensemble is statistically consistent with reality: the ensemble mean is unbiased, the expected ensemble variance equals the expected mean-squared error of the ensemble mean, and the variance of the ensemble members matches the variance of the truth. Equivalently, forecasts that assign probability $p$ to an event verify with relative frequency $p$. However, climatological consistency is not sufficient for users acting on individual forecasts. A natural extension is to assess reliability conditional on the forecast itself, by examining whether, on average, larger ensemble means imply larger observed values, larger spreads imply larger forecast errors, or higher probabilities imply higher event frequencies. This motivates conditional reliability diagnostics such as reliability diagrams and spread-error relationships. Here we show that conditional reliability diagnostics are systematically biased for finite ensemble sizes. We present a unified framework for slope attenuation caused by finite-ensemble sampling noise, which affects conditional diagnostics for ensemble means, spreads, and probabilities. Using synthetic forecasts that are perfectly reliable by construction, we isolate finite-ensemble effects. We derive analytical expressions for the expected attenuation and propose practical estimators computable directly from ensemble data. The framework is illustrated using 2-metre temperature sub-seasonal ensemble forecasts from ECMWF, where finite-ensemble slope attenuation substantially affects the spread-error relationship and tercile-based reliability diagrams. These results demonstrate that attenuated conditional slopes should not be interpreted as evidence of forecast deficiencies unless finite-ensemble effects are explicitly taken into account.

We calculate the effects of polariton bi- and multistability in a semiconductor Bragg microcavity with multiple quantum wells and a chiral photonic crystal on the upper mirror for resonant coherent pumping normal to the structure. Even if the system is not optimized for obtaining photoluminescence with a high degree of circular polarization in the spontaneous mode, it is shown that linear-polarized pumping can cause nonlinear switching to states with a degree of circular polarization of polaritons up to 90%. Calculations were performed in both the mean-field and self-consistent approximations, accounting for the difference in exciton density among the microcavity's quantum wells.

We study relaxation-based approaches for conserving mass and energy in the numerical solution of Schrödinger-Poisson (SP) type systems. Relaxation-based methods offer a general approach that can be applied as post-time step processing to achieve conservation with any time-stepping scheme. Here we study two types of relaxation techniques applied to implicit-explicit Runge-Kutta schemes, with Fourier collocation in space. We also study SP equations with time-varying coefficients (which appear naturally in cosmology) where energy is not conserved but satisfies a balance equation. We show that the fully-discrete system conserves both mass and energy (or satisfies the balance equation in case of time-varying coefficients), up to rounding errors. The effectiveness of these methods is demonstrated via numerical examples, including a three-dimensional cosmological simulation.

The three dimensional cubic defocusing nonlinear wave equation is known to be ill-posed for general low regularity initial data. Well-posedness can however be recovered globally in time on a probabilistic level, provided the Fourier coefficients of the random initial data follow a sub-Gaussian law, an assumption on which the available proofs rely heavily. In this article we perform numerical simulations of the one dimensional fractional cubic defocusing wave equation in a periodic setting, with low regularity random initial data drawn either from a Gaussian or from a heavy tailed Student law. Besides illustrating probabilistic well-posedness and norm inflation in both the energy subcritical and supercritical regimes, our simulations display no qualitative difference between the heavy tailed and the sub-Gaussian cases. This leads us to conjecture that probabilistic well-posedness holds under the sole assumption that the Fourier coefficients are centered with finite variance.

This e-letter re-evaluates the epidemiological impact of nationwide mass antigen testing in Slovakia. While initial reports \cite{Pavelka} proposed a causal link between these campaigns and declining viral prevalence, granular re-analysis reveals a significant temporal mismatch. We argue that the proclaimed success represents a conceptual nexus lacking empirical support; shifts in the effective reproduction number ($R_t$), case trajectories, and mortality rates do not align with the testing rounds. Crucially, the mortality-to-hospital admission ratio exhibits a distinct inverse relationship with the interventions. Rather than providing a clinical benefit, the testing campaigns were followed by increased mortality and a strained healthcare system. We contend that these adverse outcomes were a direct consequence of the testing policy, which sustained higher overall mobility levels compared to the United Kingdom. By overattributing causality to mass testing, a spurious nexus was constructed, obscuring the true drivers of the pandemic and its socio-economic consequences.

A key diagnostic in warm-dense matter (WDM) experiments is X-ray Thomson scattering (XRTS), but its interpretation is often limited by complex instrument effects and the high computationally expensive combinations of microscopic models with detector simulations. We present a proof-of-principle implementation of an event-driven approach to XRTS modelling, inspired by particle physics event-generators. Instead of computing the spectra via forward models, individual scattering events are sampled from the differential cross section and sent through a spectrometer simulation. This provides a statistically consistent representation that preserves full kinematic information and enables flexible and geometry-aware analysis. We demonstrate the feasibility and physical consistency of the method for non-resonant XRTS in a synthetic setup. By decoupling event generation from detector-level analysis, the framework allows efficient reuse of the sampled events and reduces computational overhead associated with repeated evaluations. The method is model-agnostic and establishes a new connection between particle-physics event generation techniques and WDM diagnostics, providing a scalable foundation for advanced XRTS analysis and inference.

We show that propagating switching fronts mediate directional state transmission and polarity selection in a passive many-body suspension. In confined trains of slipper-shaped deformable particles in Poiseuille flow, this behavior originates from directionally biased switching between neighboring particles: owing to the fore-aft asymmetry of the slipper, an upstream particle drives switching of its downstream neighbor more effectively than in the reverse direction. A local transition from an opposite-sign pair to a same-sign pair therefore launches a streamwise front that relays the inclination sign from particle to particle. A minimal coarse-grained model with local bistability and directional coupling captures front propagation and arrest. In periodic trains, the fronts coarsen into a uniformly polarized state, whereas in long open trains they arrest and leave persistent polarized domains. Our results point to local bistability and directional coupling as a route to collective polarization in passive many-body systems.

Deep-lying core electrons carry highly localized, site-specific information that forms the basis of X-ray photoelectron spectroscopy. Accurately predicting their associated core ionization potentials (IPs) is a demanding theoretical task, requiring a balanced treatment of strong orbital relaxation, electron correlation, and relativistic effects. Over the years, a variety of approaches have been developed, ranging from state-specific wave function methods to linear-response formalisms and Green's function techniques. However, their assessment has often relied on comparisons with experiment, where multiple sources of error (basis set incompleteness, relativistic corrections, and vibrational effects) are entangled, making it difficult to isolate the performance of correlation treatments. In the present work, we establish a consistent, theory-based benchmark for core IPs by computing 84 non-relativistic values (73 second-row and 11 third-row IPs) at the full configuration interaction level within the core-valence separation approximation, using large correlation-consistent basis sets augmented with tight-core and diffuse functions (aug-cc-pCVXZ). These results define theoretical best estimates within a fixed finite basis set, providing a chemically accurate reference for method development and validation. Importantly, our dataset allows for systematic, theory-versus-theory comparisons that disentangle correlation and relaxation effects from other physical contributions. On this basis, we assess the performance of widely used approximate methods, including equation-of-motion coupled-cluster approaches up to the inclusion of quadruple excitations, the one-shot $G_0W_0$ scheme, as well as state-specific methods.

Millimeter-sized particles trapped at the surface of a liquid bath attract each other through the deformation of the liquid-air interface, a phenomenon known as "the Cheerios effect". We consider here a situation similar at first sight: the interaction between two millimeter-sized particles trapped in an horizontal soap film. In this geometry, the deformation of the film due to the weight of one particle extends over the entire system size, which induces an extremely long-ranged attraction. Combined with the low viscous friction in the film, this leads to intricate particle orbits, lasting up to ten seconds before the two particles eventually collide. By tracking the particles dynamics, we measure the force exerted by each particle on the other, and we develop a theoretical model. Because the interface deformation induced by a particle depends on its position in the soap film, the attractive force has two features that fundamentally depart from classical interaction forces. The force exerted by one particle on the other differs both in direction and magnitude from the reverse interaction, with an asymmetry reaching 150% when one particle is close to the center and the other one close to the frame. Reciprocity is recovered when both particles are close to the film center. These results are a original example of non-reciprocal effective interactions due to boundary conditions.

Quantum advantage, broadly understood as the ability of quantum systems to significantly outperform their classical counterparts, underpins current interest to quantum technologies and is a topic of active investigation. In many situations, its existence is subject to debate, and the areas of supremacy of large-scale quantum systems are not well defined. Here, we uncover a novel niche where quantum advantage can be clearly defined and proven. We study a time-optimal transfer of excitations in the lattice involving both nearest-neighbor and longer-range couplings. We prove that the quantum-mechanical property of a particle to propagate along several trajectories simultaneously speeds up the transfer process, which takes a shorter time compared to any particular trajectory and thus provides a clear example of quantum advantage.

We demonstrate that graph neural network (GNN) embeddings of injector configurations provide a practical operational coordinate system for the Continuous Electron Beam Accelerator Facility (CEBAF) injector at Jefferson Lab. Using 137,389 snapshots spanning January 2022 through March 2023, we show that injector operation occupies a small number of persistent, well-separated neighborhoods in a 16-dimensional learned state space rather than a featureless continuum. Density-based clustering identifies ten recurring operating regimes with strong operational run alignment, and regime persistence statistics confirm that these regimes are stable over timescales of hours to weeks. Large relocations between neighborhoods are rare and episodic; 99.6% of one-hour operating windows fall within an empirically derived jitter baseline. Geometric outlier screening narrows a year-long dataset to a small set of intervals warranting operational review, and nearest-neighbor retrieval enables case-based reasoning over the historical archive. A controlled beam study validates that deliberate injector reconfiguration traces coherent, interpretable trajectories in embedding space. Together these capabilities demonstrate that machine-state embeddings support holistic operational monitoring in ways that single-channel inspection cannot.